Triple

T9090322
Position Surface form Disambiguated ID Type / Status
Subject Kaposvár E217864 entity
Predicate hasTwinTown P919 FINISHED
Object Tartu, Estonia E43129 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Tartu, Estonia | Statement: [Kaposvár, hasTwinTown, Tartu, Estonia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tartu, Estonia
Context triple: [Kaposvár, hasTwinTown, Tartu, Estonia]
  • A. Tartu chosen
    Tartu is Estonia’s second-largest city and a historic cultural and intellectual center, best known as the country’s main university town.
  • B. Tallinn
    Tallinn is the capital and largest city of Estonia, a historic Baltic Sea port known for its well-preserved medieval Old Town and strategic maritime location.
  • C. Maardu
    Maardu is an industrial town in northern Estonia, located just east of the capital Tallinn in Harju County.
  • D. Narva
    Narva is a historic city in northeastern Estonia on the border with Russia, known for its strategic military importance and well-preserved fortress overlooking the Narva River.
  • E. Pärnu
    Pärnu is a coastal city in southwestern Estonia known as a popular summer resort and spa destination on the Baltic Sea.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ca83d8ab5881909d8fddae363b32b1 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cc965971f88190acffbf204c11832b completed April 1, 2026, 3:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69d017ecc8b4819094dc0b009484ec00 completed April 3, 2026, 7:41 p.m.
Created at: March 30, 2026, 7:14 p.m.